# Scalable Bayesian Preference Learning for Crowds

arXiv: https://arxiv.org/abs/1912.01987 · HTML: https://mlsanity.com/paper/1912.01987/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels](https://mlsanity.com/paper/2203.07788/) (2022)
- [Ranking with Confidence for Large Scale Comparison Data](https://mlsanity.com/paper/2202.01670/) (2022)
- [Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels](https://mlsanity.com/paper/2301.00545/) (2023)
- [Improving Maximum Likelihood Difference Scaling method to measure inter content scale](https://mlsanity.com/paper/2203.13186/) (2022)
- [Towards Abstractive Timeline Summarisation using Preference-based Reinforcement Learning](https://mlsanity.com/paper/2211.07596/) (2022)
- [PARWiS: Winner determination under shoestring budgets using active pairwise comparisons](https://mlsanity.com/paper/2603.01171/) (2026)

## Abstract (arXiv)

We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels. Peoples' opinions often differ greatly, making it difficult to predict their preferences from small amounts of personal data. Individual biases also make it harder to infer the consensus of a crowd when there are few labels per item. We address these challenges by combining matrix factorisation with Gaussian processes, using a Bayesian approach to account for uncertainty arising from noisy and sparse data. Our method exploits input features, such as text embeddings and user metadata, to predict preferences for new items and users that are not in the training set. As previous solutions based on Gaussian processes do not scale to large numbers of users, items or pairwise labels, we propose a stochastic variational inference approach that limits computational and memory costs. Our experiments on a recommendation task show that our method is competitive with previous approaches despite our scalable inference approximation. We demonstrate the method's scalability on a natural language processing task with thousands of users and items, and show improvements over the state of the art on this task. We make our software publicly available for future work.